System and method for efficiently providing a recommendation

Abstract

A system and method for applying a first filter and a second filter, such as a recommendation and a constraint filter, to a plurality of items, including determining a cost of applying the first filter and the second filter to the plurality of items, and determining an order of applying the first and second filters based on the cost of applying the first and second filters.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 09/404,597, filed Sep. 24, 1999, now U.S. Pat. No. 7,461,058, which is incorporated by reference herein in its entirety.

BACKGROUND

1. Field of the Invention

This invention relates generally to data processing systems, and more particularly, collaborative filtering and recommender systems.

2. Background

Recommender systems are becoming widely used in e-commerce business activities. For example, systems that make personalized recommendations are used as a marketing tool to turn “window shoppers” into buyers, increase cross-sells and up-sells, and deepen customer loyalty. Recommender systems allow e-commerce operators to take advantage of customer databases to provide valuable personalized service to customers.

Current recommender systems can make generic recommendations to customers, but they do not take into account many of the business rules that merchandisers wish to implement, such as “don't recommend an item that is out of stock,” “don't recommend an item from a category that the customer has not selected,” “don't recommend items that are not in season,” or “don't recommend inappropriate items to minors.” In other words, current recommender systems base recommendations solely on the customer preference data.

Existing recommender systems allow only the simplest form of filtering, and they do it one of two ways, prefiltering or postfiltering.

Prefiltering requires a constraint system that discovers acceptable items and then submits all discovered items to a prediction system that makes recommendations from this subset. Prefiltering has some serious practical limitations, however. For example, gathering the list of acceptable items is difficult to accomplish efficiently as the list of acceptable items may be very large since it is selected from the whole item catalog.

Postfiltering also requires a system to filter the recommendation list. Postfiltering requires that the recommendation system produce more recommendations than actually required. The oversized list is passed to a constraint system, which then removes unacceptable items. Although postfiltering may avoid the problem of having to select items from a large list, it may fail to provide recommendations if the postfiltering eliminates all items.

BRIEF SUMMARY

Methods and systems consistent with the present invention provide a recommendation server that receives a recommendation request from a user of a client computer. The recommendation server contains software to provide recommendations to the user. To provide the recommendations, the recommendation server applies a constraint filter and a recommendation filter to a set of items.

In accordance with methods and systems consistent with the present invention, a method for providing a recommendation list specifies a constraint filter to select items satisfying a constraint, selects the items that satisfy the constraint filter, computes predicted values based on a recommendation filter, and appends the items meeting predetermined criteria.

In accordance with methods and systems consistent with the present invention, a method for applying a recommendation filter and a constraint filter to a plurality of items is provided. The method receives a recommendation request from a user, specifies a constraint filter to select ones of the items satisfying a constraint, and determines the order of the filters based on a cost of the filters. The method applies the constraint filter first when the cost of the constraint filter is lower than the cost of the recommendation filter. Otherwise, the method applies the recommendation filter first when the cost of the recommendation filter is lower than the cost of the constraint filter.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an implementation of the invention and, together with the description, serve to explain the advantages and principles of the invention. In the drawings,

FIG. 1 depicts a data processing system suitable for practicing methods and systems consistent with the present invention;

FIG. 2 depicts a more detailed diagram of the client computer depicted in FIG. 1;

FIG. 3 depicts a more detailed diagram of the recommender server depicted in FIG. 1;

FIG. 4 depicts a flow chart of the steps performed by the data processing system of FIG. 1 when initiating the constraint process consistent with methods and systems of the present invention;

FIG. 5 depicts a flow chart of the steps performed by the data processing system of FIG. 1 when initiating the recommender process in accordance with methods and systems consistent with the present invention;

FIG. 6A depicts a constraint tree consistent with methods and systems of the present invention;

FIG. 6B depicts an recommendation request form interface consistent with methods and systems of the present invention;

FIG. 6C depicts an output recommendation list interface consistent with methods and systems of the present invention; and

FIG. 7 depicts a constraint filter and recommendation filter consistent with methods and systems of the present invention.

DETAILED DESCRIPTION

The following detailed description of the invention refers to the accompanying drawings. Although the description includes exemplary implementations, other implementations are possible, and changes may be made to the implementations described without departing from the spirit and scope of the invention. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. Wherever possible, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts.

Overview

Recommender systems provide recommendations to users based on various attributes. For example, collaborative filtering (CF) systems are a specific type of recommender system that recommend items to a user based on the opinions of other users. In their purest form, CF systems do not consider the content of the items at all, relying exclusively on the judgment of humans of the item's value. In this way, CF systems attempt to recapture the cross-topic recommendations that are common in communities of people.

Commercial applications of ratings-based collaborative filtering now exist in a variety of domains including books, music, grocery products, dry goods, and information. One example of a CF-system is the GroupLens Research system that provides a CF for Usenet news and movies. More information on CF technology may be found at <http://www.netperceptions.com>, hereby incorporated by reference.

To use the recommendation system, an operator may first create a constraint using a constraint language that allows different business rules to be described in textual form. For example, to select a candidate from a set of red items, a constraint may be: “candidate isa red-thing.” To select a candidate from a set of movies that are both comedies and not r-rated, a constraint may be: “candidate isa comedy and not candidate isa r-rated.”

An item may be anything for which a user may recommend. For example, in the domain of movies, each movie may be an item. An item may be assigned arbitrarily to one or more categories. For example, a fiction book may be a member of the “Fiction” category. Category membership may represent any attribute of a user or item. For example, an item that is in stock may be a member of the “in stock” category or an item that is red may be a member of the “red-things” category. This type of categorization allows the recommendation system to apply a constraint filter based on any attribute or combination of attributes of the item. A constraint filter is a software with a complex boolean expression as an attribute that the recommendation system uses to restrict items.

A constraint may also consist of free variables. A free variable is a placeholder for an attribute that can be determined at execution time. For example, to provide the user with the ability to choose a category when applying a constraint, a constraint may be: “candidate isa X,” where the user inputs X at runtime.

Once the operator creates the constraint, the recommendation system may begin accepting recommendation requests from a user. To use the recommendation system, a user may access a web site with instructions and web pages for the user to fill out to use the recommendation system. Upon accessing the web site, the user enters a recommendation request including values for various free variables and the number of items desired. The recommendation request may include the values for the free variables for the constraint filter, the number of items desired and user information. One skilled in the art will appreciate that other methods may be used to obtain a recommendation request, such as a telephone call center or manual entry.

Once the recommendation system receives the recommendation request, the system next determines the order of a constraint filter and a recommendation filter to apply to the received recommendation request. The constraint filter determines whether an item satisfies a constraint and the recommendation filter determines a predicted value of the item based on historical or statistical information. One type of recommendation filter is the well-known collaborative filtering (CF) technique.

The recommendation filter may compute a predicted value to determine if an item should be recommended. A predicted value is a number that rates an item according to certain criteria. For example, a predicted value may be used to rank an item based on recommendations from similar users of the recommendation system. The predicted value is essentially an estimate of how much a user is likely to enjoy an item and may be determined, for example, by a CF technique. One skilled in the art will appreciate that the predicted value may be determined in a number of different ways, such as previous purchases, previous comments or a particular rating given by the user.

To determine the order of filters to apply, the recommendation system determines the cost of applying successively each filter to all items. The cost of applying each filter is explained below. For example, if the cost to apply a constraint filter before a recommendation filter is lower than the other sequence, the recommendation system may choose to apply the constraint filter first. Preferably, the recommendation system provides a recommendation to a user with the lowest cost.

Once the order of the filters is determined, the recommendation system recommends a list of items to the user that the user may be interested in based on the recommendation filter and also passes the constraint filter. If the cheapest method is to apply the constraint filter first, each time an item passes the constraint, it becomes a potential candidate for a recommendation list. The candidate is then passed to the recommendation filter. If the candidate passes the recommendation filter, the candidate and the candidate's predicted value are appended to a recommendation list.

System Components

FIG. 1 depicts a data processing system 100 suitable for practicing methods and systems consistent with the present invention. Data processing system 100 comprises a client computer 112 connected to recommendation server 120 via a network 130, such as the Internet. The user uses client computer 112 to provide various information to recommendation server 120.

Recommendation server 120 transmits and receives web pages from a browser on client computer 112 using hypertext markup language (HTML), Java or other techniques. These web pages may-include-images or instructions to obtain recommendation requests from a user. Recommendation server 120 also contains a database that stores various data, such as constraint filters, recommendation filters and items, further described below.

Although only one client computer 112 is depicted, one skilled in the art will appreciate that data processing system 100 may contain many more client computers and additional client sites. One skilled in the art will also appreciate that client computer 112 may come with the recommendation server software already installed.

FIG. 2 depicts a more detailed diagram of client computer 112, which contains a memory 220, a secondary storage device 230, a central processing unit (CPU) 240, an input device 250, and a video display 260. Memory 220 includes browser 222 that allows users to interact with recommendation server 120 by transmitting and receiving files. An example of a browser suitable for use with methods and systems consistent with the present invention is the Netscape Navigator browser, from Netscape.

As shown in FIG. 3, recommendation server 120 includes a memory 310, a secondary storage device 320, a CPU 330, an input device 340, and a video display 350. Memory 310 includes recommendation software 312, which determines if an item should be recommended to the user by applying a constraint filter 316 and a recommendation filter 318, described below. Recommendation software 312 also interacts with client computer 112 for transmitting and receiving files, such as HTML files or Java files. To interact with client computer 112, recommendation software may include a web server. Although a web server is described in this particular embodiment of the recommendation server, recommendation server 120 may interact with a client in other ways such as, voice prompts, call centers, or kiosks. Memory 310 also includes constraint builder software 314, which creates constraints that are used by recommendation software 312 to recommend an item to the user. Recommendation software 312 and constraint builder software 314 also provide access to database 322 in secondary storage device 320.

Secondary storage device 320 includes grammar file 322 containing a set of rules which map textual constraints to their internal representation in the constraint filter. Secondary storage device also includes database 324 with constraint table 326 that stores built constraints to use with recommendation software 312 and item table 328 with attribute information about each item. For example, item table 328 could store a category identification, item number, and number in stock.

Although aspects of the present invention are described as being stored in memory, one skilled in the art will appreciate that these aspects may be stored on or read from other computer-readable media, such as secondary storage devices, like hard disks, floppy disks, and CD-ROM; a carrier wave received from a network like the Internet; or other forms of ROM or RAM. Additionally, although specific components and programs of client computer 112 and recommendation server 120 have been described, one skilled in the art will appreciate that these may contain additional or different components or programs.

Constraint Creation Process

FIG. 4 depicts a flow chart of the steps performed when creating a constraint on recommendation server 120. The constraint creation process is initiated, for example, by an operator inputting a textual constraint into constraint builder software 314 (step 402). The constraint may contain free variables or bound expressions. After the operator inputs the constraint, the builder software checks the syntax of the constraint (step 404). For example, an acceptable syntax may include logical expressions or relational expression. That is, constraint builder software verifies that the operator created a valid constraint to signify a possible business rule. Logical expression include, for example, AND, OR, or NOT boolean expressions. Relational expressions include, for example EQUAL TO, GREATER THAN, LESS THAN or ISA.

Once the constraint is verified for the correct syntax, constraint builder software may translate the textual constraint into, for example, a constraint in a tree structure format (step 406). Constraint builder software 314 includes the well-known yacc parser to translate the textual constraint. The builder software reads grammar specification file 322 and generates a constraint tree consisting of objects by using the grammar specifications applied to the textual constraint. The new constraint tree is in a format acceptable to recommendation software 312.

Grammar specification file 322 consists of many different object formats to create the constraint tree, such as logical expression objects, relational expression objects or leaf objects. Logical expression objects are a type of boolean expression, such as AND, OR or NOT. Relational expression objects compare two leaf objects. Leaf objects represent any entity in the application domain. A leaf may be a category leaf, free variable leaf, candidate leaf, or subject leaf. A category leaf represents at least one item in database 324, such as “Thriller” or “Shoes.” A free variable leaf is essentially a placeholder that is specified at execution time by the user or operator. The free variable leaf stores a variable name, which is bound to an actual entity in the application domain during the recommendation process, further described below. Similar to the free variable leaf, the candidate leaf is also a placeholder. The candidate leaf represents the actual item discovered by the recommendation filter. Finally, a subject leaf is a placeholder for the user who requested the recommendation. For example, a user may have an attribute of being 14 years old. Thus, leaf objects in the constraint tree may reflect this attribute.

For example, a textual constraint created by an operator to produce recommendations for movies that are now playing, and are of a genre selected by a user, and prohibits the recommendation of r-rated movies to minors may be:

X: (candidate isa movie) and (candidate isa X) and (candidate isa NowPlay) and (not (subject isa minor) and (candidate isa R-rated)).

Constraint builder software 314 would translate the textual constraint to the tree of objects depicted in FIG. 6A.

Once constraint builder software 314 translates the textual constraint to a constraint tree, the constraint tree is placed as an entity in constraint table 326 (step 408). The constraint tree is used later by recommendation software 312 during the recommendation process. This completes the constraint creation process.

Recommendation Process

FIG. 5 depicts a flow chart of the steps performed when initiating the recommendation process in accordance with methods and systems consistent with the present invention. The recommendation process is initiated, for example, by a user accessing recommendation server 120 (step 502). Once accessed, recommendation software 312 transmits a recommendation request page to client computer 112 (step 504). The request page may be in HTML. One skilled in the art will appreciate that the inquiry page may be designed in other formats, such as Visual Basic or Java. The request page may include a category selection field, e.g., fields 602 and 604, desired results field 606 for the user to fill out, and a submit button 608, as shown in FIG. 6B. Once the request page is displayed on browser 222, the user may select a category and enter the number of results and submit the request to recommendation server 120 by pressing button 608 (step 506). When button 608 is pressed, browser 222 transmits the category and number or results information to recommendation server 120 using the well-known Hypertext Transport Protocol (HTTP).

Once received at recommendation server 120, recommendation software 312 binds the free variables in the appropriate constraint with the category selected by the user and the number of items desired (step 508). To do so, recommendation software 312 first locates the appropriate constraint in constraint table 326. The constraint may be found in constraint table 326, for example, by a tag appended to the recommendation request indicating the constraint. Once located, recommendation software 312 descends the constraint tree to locate free variable objects. Once a free variable object is found, recommendation software 312 copies the information from the recommendation request to the free variable. For example, in FIG. 6B, if a user selected “rock” and “jazz” as the categories to search, step 508 generates an array of length two that contains the object representing jazz and rock with a corresponding index number. The array looks as follows:

Index Object

1     Jazz
2     Rock

Step 508 uses the array when descending the constraint tree searching for a free variable object. When a free variable object is located, the index is matched and the corresponding object is copied to the free variable object. Ultimately, recommendation software 312 will examine each node in the constraint tree to locate all free variables and store corresponding information in each free variable. Although two free variables were used in FIG. 6B, one skilled in the art will appreciate that many more free variables may exist in the recommendation request.

After the free variables in the constraint tree are bound, recommendation software 312 examines each item in item table 328 for an item to recommend to the user. The process begins with recommendation software determining the lowest cost method to complete a recommendation request (step 510). To do so, recommendation software 312 determines the cost of applying constraint filter 316 and recommendation filter 318 in different orders to the items. As shown in FIG. 7, each filter has a generation interface that produces items and a rejection interface that determines whether a particular item is suitable for recommendation to the user. Each filter is applied sequentially. The generation interface is called on the first filter, and the produced items are passed to the rejection interface on the second filter.

If the cost of generating a sufficient number of items by applying the generation interface of the-constraint filter before applying the rejection interface of the recommendation filter is lower than applying the generation interface of the recommendation filter before applying the rejection interface of the constraint filter, then recommendation software 312 will apply the generation interface of the constraint system first to item table 328. Otherwise, recommendation software 312 applies the generation interface of the recommendation filter first. The cost may be approximated by the following equation:
Cost=(number of results required/probability that a randomly selected item will pass the rejection interface of the second filter)*(cost of applying the generation interface of the first filter to generate a single item+cost of applying the rejection interface of the second filter to a single item)

Once the order of the filters is determined, recommendation software 312 determines if enough items have been located (step 512). That is, recommendation software continues to discover new items in item table 328 until the required number of items requested from the user has been reached. Once an item has been discovered in item table 328, the item is evaluated (step 514). Evaluation occurs by applying the constraint filter to the item. Items that pass the constraint filter will be passed to the recommendation filter (step 516). An item passes the constraint filter when it satisfies the constraints conditions. If an item does not pass the constraint filter, the item is discarded and not recommended.

Next, the recommendation filter may compute a predicted value for the item (step 518). Also in step 518, each item whose predicted value is at least a threshold value is appended to a result list for display on client computer 112. The results may be displayed in HTML.

FIG. 6C depicts an output interface 620 presented to the user after submitting the recommendation request in FIG. 6B. Output interface 620 contains a recommendation list 622. For example, the user may select an item from the list to purchase.

CONCLUSION

Methods, systems, and articles of manufacture consistent with the present invention provide a recommendation server that receives a recommendation request from a user of a client computer. The recommendation server contains software to provide recommendations to the user. To provide the recommendations, the recommendation server applies a constraint filter and a recommendation filter on a set of items.

The foregoing description of an implementation of the invention has been presented for purposes of illustration and description. It is not exhaustive and does not limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the invention. For example, the described implementation includes software but the present invention may be implemented as a combination of hardware and software or in hardware alone.

Claims

1. A method for applying filters to a plurality of items in a data processing system comprising:

receiving a recommendation request;

specifying a constraint filter that selects items satisfying a constraint;

determining an order of the filters based on a cost of applying the filters, including:

applying the constraint filter first when a cost of applying the filters when the constraint filter is applied first is lower than a cost of applying the filters when a recommendation filter is applied first, wherein the constraint filter determines if each item satisfies the constraint, and

applying the recommendation filter first when the cost of applying the filters when the recommendation filter is applied first is lower than the cost of applying the filters when the constraint filter is applied first, wherein the recommendation filter assigns a predicted enjoyment value to each item.

2. The method of claim 1, wherein the determining the order of the filters further comprises:

analyzing a cost for applying the filters to the items; and

determining a probability that the items will pass both filters.

3. The method of claim 1, wherein the cost of applying the filters is determined by the equation:

C=M/P(G+R),

wherein M is a number of desired items, P is a probability that the items will pass a second applied filter, G is a time to retrieve the items, and R is a time to decide if the items will pass the second applied filter.

4. The method of claim 1, further comprising:

calculating the cost of applying the filters based at least on a probability that a randomly selected item of the plurality of items will pass a second applied filter of the recommendation and constraint filters.

5. The method of claim 4, wherein the calculating is further based on a cost of applying a first applied filter of the recommendation and constraint filters to generate a single item.

6. The method of claim 4, wherein the calculating is further based on a cost of applying a second applied filter of the recommendation and constraint filters to a single item.

7. The method of claim 4, wherein the calculating is further based on a number of results required.

8. A method, comprising:

determining an order for applying a constraint filter and a recommendation filter to a plurality of items, comprising:

calculating a cost of applying the constraint filter and the recommendation filter in a first order based at least on

(i) a number of results required,

(ii) a probability that a randomly selected item of the plurality of items will pass a second applied filter of the constraint filter and the recommendation filter,

(iii) a cost of applying a first applied filter of the constraint filter and the recommendation filter to generate a single item, and

(iv) a cost of applying the second applied filter of the constraint filter and the recommendation filter to the single item; and

generating a recommendation list, comprising applying the constraint filter and the recommendation filter to the plurality of items according to the determined order, wherein the constraint filter determines if each item satisfies a constraint and the recommendation filter assigns a predicted enjoyment value to each item.

9. The method of claim 8, further comprising:

receiving a recommendation request.

10. The method of claim 8, further comprising:

displaying the recommendation list.

11. The method of claim 8, wherein the determining an order comprises calculating the cost using the equation: cost=(number of results required/probability that a randomly selected item will pass the second applied filter)*(cost of applying the first applied filter to generate a single item+cost of applying the second applied filter to the single item).

12. A system, comprising:

a recommendation server comprising a processing device configured to:

receive a recommendation request,

determine an order for applying a constraint filter and a recommendation filter to a plurality of items based on a cost of applying the constraint filter and the recommendation filter, wherein the constraint filter determines if each item satisfies a constraint and the recommendation filter assigns a predicted enjoyment value to each item, and

generate a recommendation list, the generating comprising applying the constraint filter and the recommendation filter in the determined order.

13. The system of claim 12, wherein the cost is based at least on a probability that a randomly selected item of the plurality of items will pass a second applied filter of the constraint filter and the recommendation filter.

14. The system of claim 12, wherein the cost is based at least on a cost of applying a first applied filter of the constraint filter and the recommendation filter to generate a single item.

15. The system of claim 12, wherein the cost is based at least on a cost of applying a second applied filter of the constraint filter and the recommendation filter to a single item.

16. The system of claim 12, wherein the cost is based at least on a number of results required.

17. A computer program product comprising a non-transitory computer readable storage medium having control logic stored therein, the control logic, when executed, causing a processor to perform operations comprising:

receiving a recommendation request;

determining an order for applying a constraint filter and a recommendation filter to a plurality of items based on a cost of applying the constraint filter and the recommendation filter, wherein the constraint filter determines if each item satisfies a constraint and the recommendation filter assigns a predicted enjoyment value to each item; and

generating a recommendation list, the generating comprising applying the constraint filter and the recommendation filter in the determined order.

18. The computer program product of claim 17, wherein the cost is based at least on (i) a number of results required, (ii) a probability that a randomly selected item of the plurality of items will pass a second applied filter of the constraint filter and the recommendation filter, (iii) a cost of applying a first applied filter of the constraint filter and the recommendation filter to generate a single item, and (iv) a cost of applying the second applied filter of the constraint filter and the recommendation filter to the single item.

19. A system, comprising:

means for receiving a recommendation request from a user;

means for determining an order for applying a constraint filter and a recommendation filter to a plurality of items based on a cost of applying the constraint filter and the recommendation filter, wherein the constraint filter determines if each item satisfies a constraint and the recommendation filter assigns a predicted enjoyment value to each item; and

means for generating a recommendation list, comprising applying the constraint filter and the recommendation filter in the determined order.

20. The system of claim 19, wherein the cost is based at least on (i) a number of results required, (ii) a probability that a randomly selected item of the plurality of items will pass a second applied filter of the constraint filter and the recommendation filter, (iii) a cost of applying a first applied filter of the constraint filter and the recommendation filter to generate a single item, and (iv) a cost of applying the second applied filter of the constraint filter and the recommendation filter to the single item.

21. A method, comprising:

sending a recommendation request to a server; and

receiving a recommendation list generated by the server of a constraint filter and a recommendation filter in an order based on a cost of applying the constraint filter and the recommendation filter, wherein the constraint filter determines if each of a plurality of items satisfies a constraint and the recommendation filter assigns a predicted enjoyment value to each item.

22. The method of claim 21, wherein the cost is based at least on (i) a number of results required, (ii) a probability that a randomly selected item of a plurality of items will pass a second applied filter of the constraint filter and the recommendation filter, (iii) a cost of applying a first applied filter of the constraint filter and the recommendation filter to generate a single item, and (iv) a cost of applying the second applied filter of the constraint filter and the recommendation filter to the single item.

23. A method, comprising:

setting parameters for a constraint filter and a recommendation filter based on a received recommendation request;

determining respective costs of applying the constraint filter and the recommendation filter to items in a data processing system, the costs being associated with a probability a selected one of the items will pass both the constraint filter and the recommendation filter;

applying a first one of the constraint or recommendation filter having a lower one of the costs; and

applying a second one of the constraint or recommendation filter having a higher one of the costs.

24. The method of claim 23, further comprising requiring a predetermined number of requests as an aspect of the parameters.

25. The method of claim 24, further comprising basing the costs on a cost of applying the first one of the constraint filter and the recommendation filter to the first one of the items.

26. The method of claim 25, further comprising basing the costs on a cost of applying the second one of the constraint filter and the recommendation filter to the first one of the items.

27. The method of claim 23, wherein the determining of the costs is recursive.

28. The method of claim 23, wherein the recommendation filter assigns a predicted enjoyment value to each of the items, the predicted enjoyment value based at least one of previous purchases, previous comments and a particular rating.

29. The method of claim 23, further comprising associating generation interfaces and rejection interfaces with the constraint and recommendation filters.

30. A non-transitory computer readable medium having instructions stored thereon, execution of which, by a computing device, causes the computing device to perform operations comprising:

setting parameters for a constraint filter and a recommendation filter based on a received recommendation request;

determining respective costs of applying the constraint filter and the recommendation filter to items in a data processing system, the costs being associated with a probability a selected one of the items will pass both the constraint filter and the recommendation filter;

applying a first one of the constraint or recommendation filter having a lower one of the costs; and

applying a second one of the constraint or recommendation filter having a higher one of the costs.

31. A system, comprising:

means for setting parameters for a constraint filter and a recommendation filter based on a received recommendation request;

means for determining respective costs of applying the constraint filter and the recommendation filter to items in a data processing system, the costs being associated with a probability a selected one of the items will pass both the constraint filter and the recommendation filter;

means for applying a first one of the constraint or recommendation filter having a lower one of the costs; and

means for applying a second one of the constraint or recommendation filter having a higher one of the costs.